Post on 13-Nov-2014
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INTRODUCTION TO STATISTICS
Statistics
Singular sense(Cowden & Oxden) A statistical tool Used for collection, presentation, analysis &
interpretation of numerical data
Statistics
Plural sense(Proff. Horace Secrist) Aggregate of facts Affected by multiplicity of causes Numerically expressed Collected for predetermined purpose Comparable
Importance and Scope of Statistics
Statistics in Planning Statistics in State Statistics in Mathematics Statistics in Economics
Importance and Scope of Statistics
Statistics in Business and Management Statistics in Accountancy and Auditing Statistics in Industry Statistics in Insurance Statistics in Astronomy
Importance and Scope of Statistics
Statistics in Physical Sciences Statistics in Social Sciences Statistics in Biological and Medical Sciences Statistics in Psychology and Education Statistics in War
Measures of Central Tendency
Arithmetic Mean Geometric Mean Harmonic Mean Median Mode
Arithmetic Mean
Sum of set of observations divided by number of observations
Arithmetic Mean
DiscreteSingle Continuous
Geometric Mean
Set of n observations in nth root
Harmonic Mean
Reciprocal of arithmetic mean
Median
Value of the variable which divides the group into two equal parts
Median
Continuous
Single/Discrete Exact Median
Mode
Value which has greatest frequency density
Measures of Dispersion
Range Quartile Deviation Mean Deviation Standard Deviation
Range
Difference between two extreme observations
Range = Xmax - Xmin
Merits of Range
Easiest to compute Rigidly defined Requires very less calculation
Demerits of Range Not based on entire data Affected by fluctuations of sampling Cannot be used with open end
classes Not suitable for mathematical
treatment
Quartile Deviation
Measure of dispersion based on upper quartile and lower quartile
Merits of Quartile Deviation
Makes use of 50% of data, which is better than range
Can be used with open end classes
Demerits of Quartile Deviation
Affected by fluctuation of sample Not suitable for further
mathematical treatment
Mean Deviation
Arithmetic mean of the absolute deviations
Merits of Mean Deviation
Based on all observations Less affected by extreme
observations than S.D. Better measure of comparison
Demerits of Mean Deviation
Ignores sign of deviation Rarely used in sociological studies Cannot be used with open end
classes
Standard Deviation
Positive square root of the arithmetic mean of the squares of the deviations from their mean
Considered as most important and widely used measure of dispersion
Merits of Standard Deviation Rigidly defined Based on all observations Suitable for further mathematical
treatment Least affected by fluctuations of
sampling
Demerits of Standard Deviation
More affected by extreme items Relatively difficult to calculate and
understand
Correlation
A statistical measure Used to study degree of
relationship between two or more variables
Types of CorrelationCorrelation
Simple
Positive & Negative
Linear & Non-linear
Partial
Multiple
Simple Correlation
Study under only two variables. Example,Height & Weight of personFamily income & ExpenditurePrice & demand
Positive and Negative Correlation
Positive if both variables moves in same direction. Example,Day temp. & Sales of ice-creamHeight & Weight
Positive and Negative Correlation
Negative if variables move in opposite direction. Example,Price & DemandDay temp. & Sales of sweater
Linear & Non-linear Correlation
Linear if unit change in one variable bring constant change in other variable. Example,
X 1 2 3 4 5
Y 5 10 15 20 25
Linear & Non-linear Correlation
Non-linear if unit change in one variable doesn’t bring constant change in other variable. Example,
X 1 2 3 4 5
Y 4 10 12 13 20
Partial Correlation Study under two variables at a time
keeping other variables constant. Example,Relationship between production and seed quality keeping fertilizer constant
Multiple Correlation Study relationship between one
variable & combined effect of other variables. Example,Relationship between production and combined effect of seed quality & fertilizer
Methods of Studying Correlation
Scatter diagram method Karl Pearson’s method Rank correlation method Bivariate frequency method
Scatter diagram method Graphical and simplest method of
finding correlation between two variables
One variable is plotted on the horizontal axis and the other is plotted on the vertical axis
Interpretation of data
Perfect positive correlation
Interpretation of data
Perfect negative correlation
Interpretation of data
High degree of positive correlation
Interpretation of data
High degree of negative correlation
Interpretation of data
Low degree of positive correlation
Interpretation of data
Low degree of negative correlation
Interpretation of data
No correlation
Karl pearson’s method
Mathematical method for studying relationship between variables
Two methods of calculatingDirect methodActual mean method
Properties of simple correlation Symmetric Value lies between -1 and 1 Independent of change of origin and
scale Independent of unit of measurement Geometric mean of two regression
coefficient
Interpretation of correlation coefficient
Value of r
Interpretation
+1 Perfect positive correlation
-1 Perfect negative correlation
Close to +1
High degree of positive correlation
Close to -1
High degree of negative correlation
0 No correlation
Close to 0 Low degree of positive or negative correlation
Rank correlation method
Mathematical method for studying relationship between variables according to rank
Qualitative characteristics cannot be measured qualitatively but can be arranged in order
Merits of Rank correlation method
Easy to calculate Simple to understand Can be applied to any type of data
(Qualitative or Quantitative)
Demerits of Rank correlation method
Actual values are not used for calculations
Not convenient method for large samples
Role of Computer Technology in Statistics
SPSS is used by students later in their career
Can be used as an amplifierQuick computational abilities of massive figure
Can be used to produce many graphs quickly and easily